题目内容
（请给出正确答案）
A..I had made the reservation yesterday
B.he had made the reservation the day before
C.he made the reservation yesterday
D.he made the reservation the day ago
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In the 1950s, the pioneers of artificial intelligence (AI) predicted that, by the end of this century , computers would be conversing with us at work and robots would be performing our house-work. But as useful as computers are, they're nowhere close to achieving anything remotely resembling these early aspirations for human like behavior. Never mind something as complex as conversation : the most powerful computers struggle to reliably recognize the shape of an object, the most elementary of tasks for a ten-month-old kid.
A growing group of AI researchers think they know where the field went wrong. The problem, the scientists say, is that AI has been trying to separate the highest, most abstract levels of thought, like language and mathematics, and to duplicate them with logical, step-by-step programs. A new movement in AI, on the other hand, takes a closer look at the more roundabout way in which naturally came up with intelligence. Many of these researchers study evolution and natural adaptation instead of formal logic and conventional computer programs. Rather than digital computers and transistors, some want to work with brain cells and proteins. The results of these early efforts are as promising as they are peculiar, and the new nature-based AI movement is slowly but surely moving to the forefront of the field.
Imitating the brain's neural (神经的) network is a huge step in the right direction, says computer scientist and biophysicist Michael Conrad, but it still misses an important aspect of natural intelligence. "People tend to treat the brain as if it were made up of color-coded transistors" , he explains, "but it's not simply a clever network of switches. There are lots of important things going on inside the brain cells themselves. " Specifically, Conrad believes that many of the brain's capabilities stem from the patternrecognition proficiency of the individual molecules that make up each brain cell. The best way to build and artificially intelligent device, he claims, would be to build it around the same sort of molecular skills.
Right now, the option that conventional computers and software are fundamentally incapable of matching the processes that take place in the brain remains controversial. But if it proves true, then the efforts of Conrad and his fellow AI rebels could turn out to be the only game in town.
The author says that the powerful computers of today
A．are capable of reliably recognizing the shape of an object.
B．are close to exhibiting humanlike behavior.
C．are not very different in their performance from those of the 50's.
D．still cannot communicate with people in a human language.
In the 1950s, the pioneers of artificial intelligence (AI) predicted that, by the end of this century, computers would be conversing with us at work and robots would be performing our housework. But as useful as computers are, they're nowhere close to achieving anything remotely resembling these early aspirations for humanlike behavior. Never mind something as complex as conversation: the most powerful computers struggle to reliably recognize the shape of an object, the most elementary of tasks for a ten-month-old kid.
A growing group of AI researchers think they know where the field went wrong. The problem, the scientists say, is that AI has been trying to separate the highest, most abstract levels of thought, like language and mathematics, and to duplicate them with logical, step-by-step programs. A new movement in AI, on the other hand, takes a closer look at the more roundabout way in which nature came up with intelligence. Many of these researchers study evolution and natural adaptation instead of formal logic and conventional computer programs. Rather than digital computers and transistors, some want to work with brain cells and proteins. The results of these early efforts are as promising as they are peculiar, and the new nature-based AI movement is slowly but surely moving to the forefront of the field.
Imitating the brain's neural network is a huge step in the right direction, says computer scientist and biophysicist Michael Conrad, but it still misses an important aspect of natural intelligence. "People tend to treat the brain as if it were made up of color-coded transistors", he explains, "but it's not simply a clever network of switches. There are lots of important things going on inside the brain cells themselves. " Specifically, Conrad believes that many of the brain's capabilities stem from the pattern recognition proficiency of the individual molecules that make up each brain cell. The best way to build an artificially intelligent device, he claims, would be to build it around the same sort of molecular skills.
Right now, the option that conventional computers and software are fundamentally incapable of matching the processes that take place in the brain remains controversial. But if it proves true, then the efforts of Conrad and his fellow AI rebels could turn out to be the only game in town.
The author says that the powerful computers of today ______.
A．are capable of reliably recognizing the shape of an object
B．are close to exhibiting humanlike behavior
C．are not very different in their performance from those of the 50's
D．still cannot communicate with people in a human language
In the 1950s, the pioneers of artificial intelligence(AI)predicted that, by the end of this century, computers would be conversing with us at work and robots would be performing our house work. But as useful as computers are, they're nowhere close to achieving anything remotely resembling these early aspirations for humanlike behavior. Never mind something as complex as conversation: the most powerful computers struggle to reliably recognize the shape of and object, the most elementary of tasks for a ten-month-old kid.
A growing group of AI researchers think they know where the field went wrong. The problem, the scientists say, is that AI has been trying to separate the highest, most abstract levels of thought, like language and mathematics, and to duplicate them with logical, step-by-step pro grams. A new movement in AI, on the other hand, takes a closer look at the more roundabout way in which nature came up with intelligence. Many of these researchers study evolution and natural adaptation instead of formal logic and conventional computer programs. Rather than digital computers and transistors, some want to work with brain cells and proteins. The results of these early efforts are as promising as they are peculiar , and the new nature-based AI movement is slowly but surely moving to the forefront of the field.
Imitating the brain' s neural (神经的) network is a huge step in the right direction, says computer scientist and biophysicist Michael Conrad, but it still misses an important aspect of natural intelligence. "People tend to treat the brain as if it were made up of color-coded transistors," he explains. "But it's not simply a clever network of switches. There are lost of important things going on inside the brain cells themselves. "Specifically, Conrad believes that many of the brain's capabilities stem from the pattern-recognition proficiency of the individual molecules that make up each brain cell. The best way to build an artificially intelligent device, he claims, would be to build it around the same sort of molecular skills.
Right now, the notion that conventional computers and software are fundamentally incapable of matching the processes that take place in the brain remains controversial. But if it proves true, then the efforts of Conrad and his fellow Al rebels could turn out to be the only game in town.
The author says that the powerful computers of today ______.
A．are capable of reliably recognizing the shape of an object
B．are close to exhibiting humanlike behavior
C．are not very different in their performance from those of the 50's
D．still cannot communicate with people in a human language
A growing group of AI researchers think they know where the field went wrong. The problem, the scientists say, is that AI has been trying to separate the highest, most abstract levels of thought, like language and mathematics, and to duplicate them with logical, step-by-step programs. A new movement in AI, on the other hand, takes a closer look at the more roundabout way in which nature came up with intelligence. Many of these researchers study evolution and natural adaptation instead of formal logic and conventional computer programs. Rather than digital computers and transistors, some want to work with brain cells and proteins. The results of these early efforts are as promising as they are peculiar, and the new nature-based AI movement is slowly but surely moving to the forefront of the field.
Imitating the brain’s neural (神经的) network is a huge step in the right direction, says computer scientist and biophysicist Michael Conrad, but it still misses an important aspect of natural intelligence. “People tend to treat the brain as if it were made up of color-coded transistors”, he explains, “but it’s not simply a clever network of switches. There are lots of important things going on inside the brain cells themselves.” Specifically, Conrad believes that many of the brain’s capabilities stem from the pattern recognition proficiency of the individual molecules that make up each brain cell. The best way to build and artificially intelligent device, he claims, would be to build it around the same sort of molecular skills.
Right now, the option that conventional computers and software are fundamentally incapable of matching the processes that take place in the brain remains controversial. But if it proves true, then the efforts of Conrad and his fellow AI rebels could turn out to be the only game in town.
第26题：The author says that the powerful computers of today ________.
A) are capable of reliably recognizing the shape of an object
B) are close to exhibiting humanlike behavior
C) are not very different in their performance from those of the 50’s
D) still cannot communicate with people in a human language
The AlphaGo programs victory is an example of how smart computers have become. But can artificial intelligence (AI) machines act ethically, meaning can they be honest and fair？ One example of AI is driverless cars. They are already on California roads, so it is not too soon to ask whether we can program a machine to act ethically. As driverless cars improve, they will save lives. They will make fewer mistakes than human drivers do. Sometimes, however, they will face a choice between lives. Should the cars be programmed to avoid hitting a child running across the road, even if that will put their passengers at risk？ What about making a sudden turn to avoid a dog？ What if the only risk is damage to the car itself, not to the passengers？ Perhaps there will be lessons to learn from driverless cars, but they are not super-intelligent beings. Teaching ethics to a machine even more intelligent than we are will be the bigger challenge. About the same time as AlphaGo’s triumph, Microsoft’s “chatbot” took a bad turn. The software, named Taylor, was designed to answer messages from people aged 18-24. Taylor was supposed to be able to learn from the messages she received. She was designed to slowly improve her ability to handle conversations, but some people were teaching Taylor racist ideas. When she started saying nice things about Hitler, Microsoft turned her off and deleted her ugliest messages. AlphaGo’s victory and Taylor's defeat happened at about the same time. This should be a warning to us. It is one thing to use AI within a game with clear rules and clear goals. It is something very different to use AI in the real world. The unpredictability of the real world may bring to the surface a troubling software problem. Eric Schmidt is one of the bosses of Google, which owns AlphaGo. He thinks AI will be positive for humans. He said people will be the winner, whatever the outcome. Advances in AI will make human beings smarter, more able and “just better human beings.” 1. What does the author want to show with the example of AlphaGo's victory？ A. Computers will prevail over human beings. B. Computers have unmatched potential. C. Computers are man’s potential rivals. D. Computers can become highly intelligent. 2. What does the author mean by AI machines acting ethically？ A. They are capable of predicting possible risks. B. They weigh the gains and losses before reaching a decision. C. They make sensible decisions when facing moral dilemmas. D. They sacrifice everything to save human lives. 3. What is said to be the bigger challenge facing humans in the AI age？ A. How to make super-intelligent AI machines share human feelings. B. How to ensure that super-intelligent AI machines act ethically. C. How to prevent AI machines doing harm to humans. D. How to avoid being over-dependent on AI machines. 4. What do we learn about Microsoft's “’chatbot” Taylor？ A. She could not distinguish good from bad. B. She could turn herself off when necessary. C. She was not made to handle novel situations. D. She was good at performing routine tasks. 5. What does Eric Schmidt think of artificial intelligence？ A. It will be far superior to human beings. B. It will keep improving as time goes by. C. It will prove to be an asset to human beings. D. It will be here to stay whatever the outcome.
In the 1950s, the pioneers of artificial intelligence (Al) predicted that, by the end of this century, computers would be conversing with us at work and robots would be performing our housework. But as useful as computers are, they're nowhere close to achieving anything remotely resembling these early aspirations for human like behavior. Never mind something as complex as conversation: the most powerful computers struggle to reliably recognize the shape of an object, the most elementary of tasks for a ten-month-old kid.
A growing group of Al researchers think they know where the field went wrong. The problem, the scientists say, is that AI has been trying to separate the highest, most abstract levels -of thought, like language and mathematics, and to duplicate them with logical, step-by-step programs. A new movement in AI, on the other hand, takes a closer look at the more roundabout way in which nature came up with intelligence. Many of these researchers study evolution and natural adaptation instead of formal logic and conventional computer programs, Rather than digital computers and transistors, some want to work with brain cells and proteins. The results of these early efforts are as promising as they are peculiar, and the new nature-based: AI movement is slowly but surely moving to the forefront of the field.
Imitating the brain's neural (神经的)network is a huge step in the right direction, says computer scientist and biophysicist Michael Conrad, but it still misses an important aspect of natural intelligence. "People tend to treat the brain as if it were made up of color-coded transistors", he explains, "but it's not simply a clever network of switches. There are lots of important things going on inside the brain cells themselves." Specifically, Conrad believes that many of the brain's capabilities stem from the pattern recognition proficiency of the individual molecules that makeup each brain cell. The best way to build an artificially intelligent device, he claims, would be to build it around the same sort of molecular skills.
Right now, the option that conventional computers and software are fundamentally incapable of matching the processes that take place in the brain remains controversial. But if it proves true, then the efforts of Conrad and his fellow A1 rebels could turn out to be the only game in town.
The author says that the powerful computers of today ______.
A．are capable of reliably recognizing the shape of an object
B．are close to exhibiting humanlike behavior
C．are not very different in their performance from those of the 50's
D．still cannot communicate with people in a human language
Section B
Directions: There are 2 passages in this section. Each passage is followed by some questions or unfinished statements. For each of them there are four choices marked A, B, C and D. You should decide on the best choice.
Three centuries ago the French mathematician Rene Descartes predicted that it would never be possible to make a machine that thinks as humans do. In 1950, the British mathematician and computer pioneer Alan Turing declared that one day there would be a machine that could copy human intelligence in every way and prove it by passing a specialized test. In this test, a computer and a human hidden from view would be asked random same questions. If the computer were successful, the questioner would be unable to differ the machine from the person by the answers.
Inspired by Turing's theory, the first conference on AI(人工智能) was held at Dartmouth College in New Hampshire in 1956. Soon afterwards an AI laboratory was started at Massachusetts Institute of Technology by John McCarthy and Marvin Minsky, two of the nation's leading Al supporters. McCarthy also invented the Al computer language, Lisps but by the early 1990s AI itself had not been achieved. However, logic programs called expert systems allow computers to "make decisions" by interpreting data and selecting from among alternatives. Technicians can run programs used in complex medical diagnosis, language translation, mineral exploration, and even computer design.
Machinery can do better than humans physically. So can computers do mental functions in limited areas—notably in the speed of mathematical calculations. For example, the fastest computers developed are able to perform. roughly 10 billion calculations per second. But making more powerful computers will probably not be the way to create a machine capable of passing the Turing test. Computer programs operate according to set procedures, or logic steps, called algorithms(运算法则). In addition, most computers do serial processing; operations of recognition and computation are performed one at a time. The brain works in a manner called parallel processing, performing operations at the same time. To achieve simulated parallel processing, some super-computers have been made with multiple processors to follow several algorithms at the same time.
Critics of the approach insist that solving a computation does not indicate understanding something a person who solved a problem would have. Human reasoning is not based solely on rules of logic. It involves perception, awareness, emotional preferences, values, evaluation experience, the ability to generalize and weigh options, and more. Some supports of AI have, therefore, suggested that computers should be patterned after the human brain, which essentially consists of a network of nerve cells.
By the early 1990s, the closest m Al was a special silicon chip built to behave like a human brain cell. It was modeled after the internal workings of neurons (神经细胞) in the human brain context. Unlike the conventional silicon chip, which works in digital mode, the new silicon chip works in analog mode, much the way a human brain cell works.
According to Turing, a computer can prove to have human-like intelligence in a special test if ______.
A．the computer gives better answers
B．the questioner fails to give identical questions
C．the questioner can't tell between the answers of a person and a computer
D．the questioner can't find the person hidden by the computer
In a few decades, artificial intelligence (AI) will surpass many of the abilities that we believe make us special. This is a grand challenge for our age and it may require an "irrational" response.
One of the most significant pieces of news from the US in early 2017 was the efforts of Google to make autonomous driving a reality. According to a report, Google's self-driving cars clocked 1,023,330 km, and required human intervention 124 times. That is one intervention about every 8,047 km of autonomous driving. But even more impressive is the progress in just a single year: human interventions fell from 0.8 times per thousand miles to 0.2, a 400% improvement. With such progress, Google's cars will easily surpass my own driving ability later this year.
Driving once seemed to be a very human skill. But we said that about chess, too. Then a computer beat the human world champion, repeatedly. The board game Go(围棋)took over from chess as a new test for human thinking in 2016, when a computer beat one of the world's leading professional Go players. With computers conquering what used to be deeply human tasks, what will it mean in the future to be human？ I worry about my six-year-old son. What will his place bе in a world where machines beat us in one area after another？ He'll never calculate faster, never drive better, or even fly more safely. Actually, it all comes down to a fairly simple question： What's so special about us？ It can't be skills like arithmetic, which machines already excel in. So far, machines have a pretty hard time emulating creativity, arbitrary enough not to be predicted by a computer, and yet more than simple randomness.
Perhaps, if we continue to improve information-processing machines, well soon have helpful rational assistants. So we must aim to complement the rationality of the machine, rather than to compete with it. If I'm right, we should foster a creative spirit because a dose of illogical creativity will complement the rationality of the machine. Unfortunately, however, our education system has not caught up to the approaching reality. Indeed, our schools and universities are structured to mould pupils to be mostly obedient servants of rationality, and to develop outdated skills in interacting with outdated machines. We need to help our children learn how to best work with smart computers to improve human decision-making. But most of all we need to keep the long-term perspective in mind: that even if computers will outsmart us, we can still be the most creative. Because if we aren't, we won't be providing much value in future ecosystems，and that may put in question the foundation for our existence.
51. What is the author's greatest concern about the use of AI？
A) Computers are performing lots of creative tasks.
B) Many abilities will cease to be unique to human beings.
C) Computers may become more rational than humans.
D) Many human skills are fast becoming outdated.
52. What impresses the author most in the field of AI？
A) Google's experimental driverless cars require little human intervention.
B) Google's cars have surpassed his driving ability in just a single year.
C) Google has made huge progress in autonomous driving in a short time.
D) Google has become a world leader in the field of autonomous driving.
53. What do we learn from the passage about creativity？
A) It is rational.
B) It is predictable.
C) It is human specific.
D) It is yet to be emulated by AI.
54. What should schools help children do in the era of AI？
A) Cultivate original thinking.
B) Learn to work independently.
C) Compete with smart machines.
D) Understand how AI works.
55. How can we humans justify our future existence？
A) By constantly outsmarting computers.
B) By adopting a long-term perspective.
C) By rationally compromising with AI.
D) By providing value with our creativity.
听力原文：W: Freedom Travel. How may I help you？
M: Yes, I' d like to make a flight reservation for the twentythird of this month.
W: Okay. What is your destination？
M: Well. I'm flying to Helsinki, Finland.
W: Okay. Let me check what flights are available？ And when will you be returning.
M: Uh, well, I' d like to catch a return flight on the twentyninth. Oh, and I' d like the cheapest flight available.
W: Okay. Let me see. Urn, hmm ...
M: Yeah？
W: Well, the price for the flight is almost double the price you would pay if you leave the day before.
M: Who. Let's go with the cheaper flight. By the way, how much is it？.
W: It's only $ 980.
M: Alright. Well, let's go with that.
W: Okay. That's flight 1070 from Salt Lake City to New York, Kennedy Airport, transferring to flight 90 from Kennedy to Helsinki
M: And what are the departure and arrival time for each of those flights？
W: It leaves Salt Lake City at 10:00 A.M., arriving in New York at 4:35 P.M., then transferring to flight 90 at 5:55 P.M., and arriving in Helsinki at 8,30 A.M. the next day.
M: Alright. And, I'd like to request a vegetarian meal.
W: Sure no problem. And could I hove your name please？
(24)
A．Salt Lake City, USA.
B．New York City, USA.
C．Helsinki, Finland.
D．Stockholm, Sweden,
Artificial Intelligence
人工智能
Advanced Idea, Anticipating Incomparability[1]—on AI, Artificial Intelligence
Artificial intelligence (AI) is the field of engineering that builds systems, primarily computer systems, to perform tasks requiring intelligence. This field of research has often set itself ambitious goals, seeking to build machines that can "outlook" humans in particular domains of skill and knowledge, and has achieved some success in this aspect. The key aspects of intelligence around which AI research is usually focused include expert system[2], industrial robotics, systems and languages, language understanding, learning, and game playing, etc.
Expert System
An expert system is a set of programs that manipulate encoded knowledge to solve problems in a specialized domain that normally requires human expertise. Typically, the user interacts with an expert system in a "consultation dialogue", just as he would interact with a human who had some type of expertise—explaining his problem, performing suggested tests, and asking questions about proposed solutions. Current experimental systems have achieved high levels of performance in consultation tasks like chemical and geological data analysis, computer system configuration, structural engineering, and even medical diagnosis. Expert systems can be viewed as intermediaries between human experts, who interact with the systems in "knowledge acquisition" mode[3], and human users who interact with the systems in "consultation mode". Furthermore, much research in this area of AI has focused on endowing these systems with the ability to explain their reasoning, both to make the consultation more acceptable to the user and to help the human expert find errors in the system's reasoning when they occur. Here are the features of expert systems.
① Expert systems use knowledge rather than data to control the solution process.
② The knowledge is encoded and maintained as an entity[4]separated from the control program. Furthermore, it is possible in some cases to use different knowledge bases with the same control programs to produce different types of expert systems. Such systems are known as expert system shells[5].
③ Expert systems are capable of explaining how a particular conclusion is reached, and why requested information is needed during a consultation.
④ Expert systems use symbolic representations for knowledge and perform their inference through symbolic computations[6].
⑤ Expert systems often reason with metaknowledge.
Industrial Robotics
An industrial robot is a general-purpose computer-controlled manipulator consisting of several rigid links connected in series by revolute or prismatic joints[7]. Research in this field has looked at everything from the optimal movement of robot arms to methods of planning a sequence of actions to achieve a robot's goals. Although more complex systems have been built, thousands of robots that are being used today in industrial applications are simple devices that have been programmed to perform some repetitive tasks. Robots, when compared to humans, yield more consistent quality, more predictable output, and are more reliable. Robots have been used in industry since 1965. They are usually characterized by the design of the mechanical system. There are six recognizable robot configurations:
① Cartesian Robots[8]: A robot whose main frame consists of three linear axes[9].
② Gantry Robots[10]: A gantry robot is a type of artesian robot whose structure resembles a gantry. This structure is used to minimize deflection along each axis.
③ Cylindrical Robots[11]: A cylindrical robot has two linear axes and one rotary axis.
④ Spherical Robots[12]: A spherical robot has one linear axis and two rotary axes. Spherical robots are used in a variety of industrial tasks such as welding and material handling.
⑤ Articulated Robots[13]: An articulated robot has three rotational axes connecting three rigid links and a base.
⑥ Scara Robots: One style of robot that has recently become quite popular is a combination of the articulated arm and the cylindrical robot. The robot has more than three axes and is widely used in electronic assembly.
Systems and Languages
Computer-systems ideas like timesharing, list processing, and interactive debugging were developed in the AI research environment[14]. Specialized programming languages and systems, with features designed to facilitate deduction, robot manipulation, cognitive modeling, and so on, have often been rich sources of new ideas. Most recently, several knowledge-representation languages—computer languages for encoding knowledge and reasoning methods as data structures and procedures—have been developed in the last few years to explore a variety of ideas about how to build reasoning programs.
Problem Solving
The first big "success" in AI was programs that could solve puzzles and play games like chess. Techniques like looking ahead several moves and dividing difficult problems into easier sub-problems evolved into the fundamental AI techniques of search and problem reduction. Today's programs can play championship-level checkers and backgammon, as well as very good chess. Another problem-solving program that integrates mathematical formulates symbolically has attained very high levels of performance and is being used by scientists and engineers. Some programs can even improve their performance with experience.
As discussed above, the open questions in this area involve capabilities that human players have but cannot articulate, like the chess master's ability to see the board configuration in terms of meaningful patterns. Another basic open question involves the original conceptualization of a problem, called in AI the choice of problem representation. Humans often solve a problem by finding a way of thinking about it that makes the solution easy—AI programs, so far, must be told how to think about the problems they solve.
Logical Reasoning
Closely related to problem and puzzle solving was early work on logical deduction[15]. Programs were developed that could "prove" assertions by manipulating a database of facts, each represented by discrete data structures just as they are represented by discrete formulas in mathematical logic. These methods, unlike many other AI techniques, could be shown to be complete and consistent. That is, so long as the original facts were correct, the programs could prove all theorems that followed from the facts, and only those theorems.
Logical reasoning has been one of the most persistently investigated subareas of AI research. Of particular interest are the problems of finding ways of focusing on only the relevant facts of a large database and of keeping track of the justifications for beliefs and updating them when new information arrives.
Language Understanding
The domain of language understanding was also investigated by early AI researchers and has consistently attracted interest. Programs have been written that answer questions posed in English from an internal database, that translate sentences from one language to another, that follow instruction given in English, and that acquire knowledge by reading textual material and building an internal database. Some programs have even achieved limited success in interpreting instructions spoken into a microphone instead of typed into the computer. Although these language systems are not nearly as good as people are at any of these tasks, they are adequate for some applications. Early successes with programs that answered simple queries and followed simple directions, and early failures at machine translation, have resulted in a sweeping change in the whole AI approach to language. The principal themes of current language-understanding research are the importance of vast amounts of general, commonsense world knowledge and the role of expectations, based on the subject matter and the conversational situation, in interpreting sentences.
Learning
Learning has remained a challenging area for AI. Certainly one of the most salient and significant aspects of human intelligence is the ability to learn. This is a good example of cognitive behavior that is so poorly understood that very little progress has been made in achieving it in AI systems[16]. There have been several interesting attempts, including programs that learn from examples, from their own performance, and from being told. An expert system may perform extensive and costly computations to solve a problem. Most expert systems are hindered by the inflexibility of their problem-solving strategies and the difficulty of modifying large amounts of code. The obvious solution to these problems is for programs to learn on their own, either from experience, analogy, and examples or by being "told" what to do.
Game Playing
Much of the early research in state space search was done using common board games such as checkers, chess, and the 15-puzzle. In addition to their inherent intellectual appeal, board games have certain properties that make them ideal subjects for this early work. Most games are played using a well-defined set of rules, which makes it easy to generate the search space and frees the researcher from many of the ambiguities and complexities inherent in less structured problems. The board configurations used in playing these games are easily represented on a computer, requiring none of the complex formalisms.
Conclusion
We have attempted to define artificial intelligence through discussion of its major areas of research and application. In spite of the variety of problems addressed in artificial intelligence research[17], a number of important features emerge that seem common to all divisions of the field, including.
① The use of computers to do reasoning, learning, or some other forms of inference.
② A focus on problems that do not respond to algorithmic solutions. This underlies the reliance on heuristic search[18]as an AI problem-solving technique.
③ Reasoning about the significant qualitative features of a situation.
④ An attempt to deal with issues of semantic meaning[19]as well as syntactic form[20].
⑤ The use of large amounts of domain-specific knowledge in solving problems. This is the basis of expert systems.
Notes
[1] 标题中的两个短语分别为两组AI，以此分别强调人工智能的最新理念无与伦比。
[2] expert system专家系统。
[3] "knowledge acquisition" mode知识获取模式。
[4] entity实体。
[5] expert system shells专家系统外壳。
[6] symloolic computation符号计算。
[7] ...by revolute or prismatic joints通过外卷的，或棱镜似的连接结合起来。
[8] Cartesian Robot直角座标机器人，主框架由三根直线轴构成。
[9] linear axes线性轴。
[10] Gantry Robot桶架式机器人Gantry桶架。
[11] Cylindrical Robot or Cylindrical Coordinate Robot柱面坐标式机器人。
[12] Spherical Robot or Spherical Coordinate Robot球坐标式机器人。
[13] Articulated Robot挂接式机器人。
[14] Computer-systems ideas like time-sharing, list processing, and interactive debugging were developed in the AI research environment. 人工智能采用了计算机系统方面的一些理念，如：时间分配，编目处理，交互式调试，等等。
[15] logical deduction逻辑推断(演绎推理的过程，在此过程中必然可从所述前提得出一个结论；从一般推向特殊的推论)。
[16] This is a good example of cognitive behavior that is so poorly understood that very little progress has been made in achieving it in AI systems. 这是一种典型的认知行为，但人们却不太了解它，以至于人工智能在这方面还没有什么发展。
[17] In spite of the variety of problems addressed in artificial intelligence research. 尽管人工智能研究中出现了各种各样的问题……
[18] heuristic search启发式搜索。
[19] semantic meaning语义(计算机语言中的每个语义成分所代表的实际操作)。
[20] syntactic form语法形式；句法形式。
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